simulation of dynamics in nitrogen mineralisation in the belowground food webs of two arable farming...

Simulation of Nitrogen Mineralization in the Below-Ground Food Webs of Two Winter WheatFieldsAuthor(s): P. C. De Ruiter, J. C. Moore, K. B. Zwart, L. A. Bouwman, J. Hassink, J. Bloem, J.A. De Vos, J. C. Y. Marinissen, W. A. M. Didden, G. Lebrink and L. BrussaardSource: Journal of Applied Ecology, Vol. 30, No. 1 (1993), pp. 95-106Published by: British Ecological SocietyStable URL: http://www.jstor.org/stable/2404274 .

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Journal of Applied Ecology 1993, 30, 95- 106

Simulation of nitrogen mineralization in the belowground food webs of two winter wheat fields P.C. DE RUITER*, J.C. MOOREt, K.B. ZWART*, L.A. BOUWMAN*, J. HASSINK*, J. BLOEM*, J.A. DE VOS*, J.C.Y. MARINISSENt, W.A.M. DIDDENt, G. LEBBINK* and L. BRUSSAARD*t *DLO Institute for Soil Fertility Research, P.O. Box 30003, 9750 RA Haren, The Netherlands; 'Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; and *Wageningen Agricultural University, Department of Soil Science and Geology, PO Box 37, 6700 AA Wageningen, The Netherlands

Summary

1. Food webs in conventional (high-input) and integrated (reduced-input) farming systems were simulated to estimate the contribution of soil microbes and soil fauna to nitrogen mineralization during the growing season. 2. Microbes accounted for approximately 95% of the biomass and 70% of total nitrogen mineralization in both management practices. Among the soil fauna, amoebae and bacterivorous nematodes were the most important contributors to nitrogen mineralization. The contribution of nematodes showed more temporal and spatial variability than the contribution of amoebae. 3. The model calculated nitrogen mineralization rates close to the observed rates for both fields and depth layers. In the integrated plot there were relatively high rates of mineralization in the 0-10cm layer compared with the 10-25 cm layer, whereas in the conventional plot no differences were observed between depth layers. 4. The impact of the functional groups on nitrogen mineralization was evaluated by calculating the effect of group deletion on total nitrogen mineralization. Ac- cording to the present model, this impact on nitrogen mineralization could exceed the simulated direct contribution to nitrogen mineralization.

Key-words: food web, nitrogen mineralization, integrated farming simulation model.

Journal of Applied Ecology (1993) 30, 95-106

Introduction

Modern arable farming has come increasingly into question because of its adverse effects on soil fertility and the environment, such as soil erosion and pollution of groundwater and surface water with agricultural chemicals. To alleviate these problems there is a need to develop alternative management practices, in which tillage and the use of chemicals, especially pesticides and fertilizers, can be reduced (see, e.g. Edwards 1990). In the Dutch Programme on Soil Ecology of Arable Farming Systems, two different management practices (called integrated and conventional farming) were compared with emphasis on soil organic matter dynamics and nitrogen mineralization (Brussaard et al. 1988; Kooistra, Lebbink & Brussaard 1989). The integrated practice

Communication no. 41 of the Dutch Programme on Soil Ecology of Arable Farming Systems.

differs from the conventional practice in (i) reduced rates of nutrient inputs with an increased use of organic fertilizers; (ii) reduced soil tillage; and (iii) reduced use of biocides and no soil fumigation (Kooistra, Lebbink & Brussaard 1989). Both management practices were applied to a 4-year crop rotation of winter wheat, sugar beet, spring barley and potato.

Special attention was paid to the role of soil microbes and soil fauna in nitrogen mineralization during the growing season in both management systems. Because of the increased use of organic manure, it was hypothesized that nitrogen mineralization might be more important in the integrated practice than in the conventional practice. On the other hand, the relatively high addition of nitrogen fertilizer to the field under conventional management might stimulate primary production, resulting in a relatively high carbon input to the soil through root turnover and residue return. This high carbon 95



96 Nitrogen mineralization in food webs

input may increase microbial activity and, consequently, the nitrogen flow through the ecosystem (Andr6n et al. 1990).

Predictions of nitrogen mineralization are usually based on knowledge of the substrate, classified by chemical characteristics such as C:N ratio and content of lignin or cellulose (e.g. Parton, Stewart & Cole 1988; Van Veen, Ladd & Frissel 1984; Verberne et al. 1990). An alternative approach is to predict nitrogen mineralization on the basis of the trophic activity of soil microbes and fauna (e.g. Andr6n et al. 1990; Hendrix et al. 1987; Hunt et al. 1987). Whether microbial activity will cause a net mineralization depends on the C:N ratio of the substrate, which may vary during the growing season, e.g. in the form of rhizodeposition. Soil fauna may contribute to nitrogen mineralization directly by releasing mineral nitrogen from microbes and other food resources, but also indirectly by stimulating microbial activity (Clarholm 1985; Coleman et al. 1978; Coleman, Reid & Cole 1983; Woods et al. 1982). Such stimulation suggests changes in rate-limiting factors, such as availability of nu- trients (Anderson, Coleman & Cole 1981), oxygen (Baath et al. 1981), or energy (Brussaard et al. 1991). Therefore, the impact of a faunal group on nitrogen mineralization might be larger than its direct contribution. The assessment of the impact

of a group of organisms on mineralization processes requires, therefore, observations on the response of the ecosystem (e.g. in terms of overall nitrogen mineralization) to the removal of the group from the ecosystem (Ingham et al. 1986; Parker et al. 1984).

In the present study, a simulation model (Hunt et al. 1987) was used to estimate carbon flows among groups of soil organisms and the concomitant nitrogen mineralization. Within taxonomic units, species were aggregated into functional groups, which were defined according to their principal food resource sensu Moore, Walter & Hunt 1988; see Fig. 1). The simulated total nitrogen mineralization rates were compared with the observed nitrogen mineralization per unit soil volume. The model was extended sensu O'Neill 1969) to include observed changes in population densities in order to simulate nitrogen mineralization in different periods of the growing season. The impact of the functional groups on the mineralization of nitrogen was assessed by simulating the effect of group deletion on overall nitrogen mineralization.

Methods

SITE DESCRIPTION

The test site was located at the Lovinkhoeve Exper-

Phytophagous Collembolans Nematodes

Predaceous Cryptostigmatid Mites

Roots Mt

1/Noncrypto- \/2Predaceous

Saprophyacttiphaou

Bacteria FlagNematodes Feeding Mites

~~~~~~Erthworms Predaceous_ <

- \4= sJ ~~~~~~~~~Nematodes |1T1

l\ hy s Amoebae

Bacteriophagous Mites

Fig. 1. Diagram of the soil food web from the Lovinkhoeve experimental farm, Marknesse, Noordoostpolder, The Netherlands. Earthworms and predatory collembola were not found in the conventional plot.



97 P. C. de Ruiter et al.

imental Farm in the Noordoostpolder (Marknesse, The Netherlands). The soil is a calcareous silt loam with pH-KCl 7-5, which was reclaimed in 1942. An- nual precipitation at the site is usually 600-950mm. The results presented in this paper are from two management practices: integrated and conventional. Integrated differs from the conventional practice in the reduction of N-fertilizer application to 50-65% of the recommended rates in the conventional practice (from 130-285 to 65-170 kg ha-1 year-' depending on the crop and integration of the use of fertilizers and manure), a reduction of pesticide application and a reduction of soil tillage (conventional: 20-25 cm ploughing, integrated: 12-15cm cultivation without inversion of the top soil). The organic matter content of the upper 20 cm of the conventional plot was 2-1% and 2-7% on the integrated plot. The C:N ratio of the soil organic matter was approximately 10 in both plots (Van Faassen & Lebbink 1990). Since 1985, the 4-year crop rotation on both plots has been the same: winter wheat, sugar beets, barley and potatoes. The data presented here are from the 1986 winter wheat crop, which followed sugar beet on both plots in 1985. A full description of the site and management practices is given by Kooistra, Lebbink & Brussaard (1989).

SOIL NITROGEN MINERALIZATION

Soil nitrogen mineralization rates were measured by incubating duplicate moist homogenized soil samples from each soil layer for 1, 6 or 12 weeks at 20'C. Samples were taken on 18 April, 20 June, 30 July, 19 August and 18 November 1986. Soils

consumption

assimilation efficiency

excretion of assimilation organic

material

production efficiency

excretion of production inorganic

_ ~~~~~~~~material

Fig. 2. Scheme relating consumption, biomass production, excretion of organic material, and excretion of inorganic material (Hunt et al. 1987).

were extracted with 1 M KCl and analysed for mineral nitrogen (ammonium and nitrate) using a Technicon Traacs 800 autoanalyzer. The increase in mineral N from 1 to 6 weeks incubation was used to calculate nitrogen mineralization rates. Mineral nitrogen after 1 week incubation was subtracted because of the effect of homogenizing the samples. The temperature response of the nitrogen mineralization was determined by separate incubation of samples at 5, 10 and 20'C showing that mineralization decreased proportionally with decreasing temperature.

DESCRIPTION OF THE MODEL

Annual average nitrogen mineralization

Feeding rates were calculated according to a scheme used by Hunt et al. (1987) (Fig. 2). The feeding rate, i.e. the rate at which material is taken from an energy source, is split into a rate at which organic material is returned to the environment in the form of faeces or prey residues, a rate at which material is incorporated into the biomass of the consumer, and a rate at which material is released in inorganic form.

For the calculation of the feeding rates of the functional groups on a yearly basis, the steady-state assumption was used, i.e. that the production rate of a group balances the rate at which material is lost through natural death and predation (Hunt et al. 1987):

F = DnatB + P eqn 1 easseprod

where F is feeding rate (kgCha-1 year-1), Dnat is specific natural death rate (year-1), B is biomass (kgCha-1), P is death rate due to predation (kgCha-1 year-), eass is assimilated carbon per unit consumed carbon, and eprod is biomass production per unit assimilated carbon. If a predator was considered to feed on more than one prey type, then both the preference of the predator for a given prey and the relative abundances of the prey types were taken into account:

wiBi Fj =

i~i _ F eqn 2

L wiBi i=l

where Fj is feeding rate on prey i (kg C ha- year-l); wi is preference for prey i relative to other prey types, and n is number of prey types.

Nitrogen mineralization was calculated per trophic interaction and depended on feeding rate, assimilation efficiency, production efficiency and the C:N ratios of food and consumer (Hunt et al. 1987):

N( 1 eprod)Feq



https://www.researchgate.net/publication/227182202_The_Detrital_Food_Web_in_a_Shortgrass_Prairie?el=1_x_8&enrichId=rgreq-ccaf96c7-e385-40de-8774-34feb3a64392&enrichSource=Y292ZXJQYWdlOzIyMzU2NDM3ODtBUzoxNjE5NjgzNDIxODM5MzdAMTQxNTYyNzY3MTAxMw==




where Nmin is nitrogen mineralization rate resulting from a trophic interaction (kgN ha-1 year- l), prey

is C:N ratio of prey, and Tpred is C:N ratio of predator. The calculations started with the feeding rates

of the predatory mites, since in the present food web description only natural death was assumed to reduce the biomass of this group (Fig. 1). The predatory losses in the groups down one trophic level were calculated from the feeding rates of the top predators. These losses were added to the non- predatory losses in order to calculate the feeding rates of the groups at this level. All feeding rates were subsequently calculated throughout the food web, working back to the primary consumers, i.e. micro-organisms and saprotrophs.

Nitrogen dynamics during the growing season

Temporal variability in temperature and biomass dynamics of the functional groups were taken into account by dividing the growing season into four periods each marked by two successive sampling dates. The specific death rates during a period were related to the mean temperature of that period by means of a Qio of 2-5, which was based on the list of Qio values given for metabolic rates by Andren et al. (1990). The biomass estimate of a functional group for a particular period was obtained by taking the mean of the biomass of the group at the two successive sampling dates, reducing the sampling errv)r as compared to the mean per sampling date. This arbitrary choice of taking the mean of two sampling dates instead of one affected the simulated mineralization rates per period but not the average mineralization rate over the complete period of observation. Increases and decreases in biomass refer to the differences between the biomass estimates for successive periods. Since these calculations are within the season, the population cannot be assumed to be in steady state. Therefore, biomass dynamics were incorporated into the model by adding the rate of change in biomass of a functional group (ABIt) to the rate of material loss according to O'Neill (1969):

F = DnatB + P + ABIt eqn 4 easseprod

where AB is change in biomass between two periods, t is time (days), and other terms are the same as those used in equation (1) except that the unit for time is days.

Effect of group deletion on overall nitrogen mineralization

The present model was used to assess the impact of the functional groups on nitrogen mineralization by removing the microbivorous and predatory groups from the model description one after another. The

primary consumers were not removed because these groups form the basic energy sources in the web. Because of the steady-state assumption underlying the model, deletion of a functional group implied that the original prey of the removed group decreased their feeding rates relative to the decrease in predation rate. The predators of the removed group maintained their feeding rates by switching to other prey. Functional groups at the same trophic level did not respond to the group deletion or, when switching of their predators led to an increased predation pressure, increased their feeding rates balancing their losses. The effect of the group deletion was expressed as the percentage with which the overall nitrogen mineralization decreased.

MODEL PARAMETERS

The model required biomass estimates, specific death rates, preference weighting factors, assimilation efficiencies, production efficiencies and C:N ratios. Parameter values and annual mean biomass estimates are given in Table 1; biomass estimates per sampling date are given by Brussaard et al. (1990). The C:N ratio adopted for the substrate of bacteria and fungi was chosen to be equal to the mean C:N ratio of the soil organic matter (10).

Biomass

On each sampling date, samples of the top 25cm were taken from three subplots within each management practice and analysed in two separate layers (0-10 and 10-25 cm). Methods used to sample and calculate biomass of bacteria and fungi are given by Hassink et al. (1991), and of protozoa, nematodes and microarthropods by Brussaard et al. (1990).

Enchytraeids were collected using a steel corer, containing 2-5-cm high plastic rings with an inner diameter of 6 cm. The cores were divided into 2-5-cm slices in the laboratory. Extraction was carried out according to O'Connor (1955), with application of increasing light and heat for half an hour after the start of the extraction and a total extraction time of 3 h. The collected animals were examined alive under the microscope and measured to the nearest millimetre. Weight was calculated using a linear relationship between length and weight, based on a number of enchytraeids which were weighted indi- vidually after freeze-drying. Dry weight was assumed to be approximately 15% of wet weight, and carbon content to be 50% of dry weight (Borkott 1989).

Earthworms were collected from 25 x 25 x 25 cm samples with layers of 5 cm depth. The soil was washed over a sieve (0-2 mm mesh). All earthworms, including small juveniles and cocoons, were re- covered and counted. Wet weight was measured on individual living worms after keeping them in



https://www.researchgate.net/publication/222244900_Dynamics_of_microbial_populations_of_a_reclaimed_polder_soil_under_a_conventional_and_reduced-input_farming_system?el=1_x_8&enrichId=rgreq-ccaf96c7-e385-40de-8774-34feb3a64392&enrichSource=Y292ZXJQYWdlOzIyMzU2NDM3ODtBUzoxNjE5NjgzNDIxODM5MzdAMTQxNTYyNzY3MTAxMw==

99 P.C. de Ruiter et al.

Table 1. Physiological parameter values and population sizes (kg C ha-1) for each functional group at the integrated (INT) and conventional (CON) management practice and at two depth layers: 0-10 and 10-25 cm (for references see text)

Population size (kg Char1) 0-10cm 10-25cm

C:N eass eprod Dnat CON INT CON INT

Microbes Bacteria 5 1-00 0*30 0.50 240-3 373-1 405 5 456 5 Fungi 10 1-00 0 30 0 50 11-7 21-0 1941 25-9

Protozoa Amoebae 5 0 95 0 40 6-00 4*52 6 09 5 50 6-19 Flagellates 5 0-95 0 40 6-00 0 25 0 35 0-22 0-36

Nematodes Herbivores 5 0.25 0-37 14-6 0-023 0-122 0-019 0-087 Bacteriovores 5 0-60 0 37 16-2 0-166 0-406 0 447 0-172 Fungivores 5 0-38 0 37 15-6 0-011 0-017 0-024 0-016 Predators 5 0.50 0 37 19-2 0-103 0-249 0-098 0-134

Microarthropods Cryptostigmatid mites 8 0 50 0 35 1-20 0-012 0 003 0.010 0-002 Non-cryptostigmatid mites 8 0*50 0-35 1-84 0-018 0.010 0-019 0-010 Bacteriovorous mites 8 0 50 0 35 1-84 0-004 0.001 0-051 0-001 Predatory mites 8 0-60 0 35 1-84 0-029 0-017 0-047 0-019 Nematophagous mites 8 0 90 0 35 1-84 0-015 0-013 0.058 0-004 Predatory Collembola 8 0 50 0-35 1-84 - 0-016 - 0-023 Fungivorous Collembola 8 0 50 0 35 1-84 0-204 0 245 0-327 0 275

Annelids Enchytraeids 5 0-28 0 40 10-00 0 077 0-190 0 465 0-233 Earthworms 5 0-20 0 45 2-40 - 8-860 - 4 740

eass, assimilation efficiency. eprod, production efficiency. Dtiat, specific natural death rate (year-1).

water for at least 2 h. According to Bostrom (1988), 12-4% of the wet weight of worms is soil material, 20% of the wet worm tissue is dry matter, and carbon and nitrogen represent 50% and 10% of the dry tissue, respectively.

Physiological parameters

Specific death rates refer to death rates under natural conditions, including death from lack of food, abiotic factors and predation by groups of organisms that were not explicitly described in the food web model (e.g. macroarthropods, nematophagous fungi). Specific death rates have the dimension 'per time', and can be derived from maintenance rates or from life-span data. Values listed in Table 1 refer to a temperature of 10'C.

Anderson & Domsch (1985) presented for microbial populations in agricultural soils coefficients of maintenance (defined as to keep the microbial population at a constant level) between 0-14 and 0-31 year-', and coefficients of microbial-C loss (including cell death and transitions to resting propagules) between 0-76 and 1-31 year-'. Kurath & Morita (1983) found specific maintenance rates in populations of a marine Pseudomonas sp. ranging from 0-18 to 0-61 year-'. From the range of 0-14-1-31

year-1, a specific death rate of 0 5 year-l was chosen for both bacteria and fungi. The yield coefficient of microbes, which is similar to the product of assimilation efficiency and production efficiency, may range from 0-10 to 0-60, depending on growth conditions (Elliott et al. 1983; Findlay et al. 1986); we chose a value of 0-30. Estimates of C:N ratios for bacteria range from 3-8 to 17-2 (Van Veen & Paul 1979; Tezuka 1990). From this range we chose a constant C:N ratio of 5, although bacteria may vary their C:N ratio according to the C:N ratio of the substrate (Tezuka 1990). Estimates of C:N ratios for fungi range from 9-4 to 44 (Van Veen & Paul 1979); following Hunt et al. (1987) we chose a C:N ratio of 10 for fungi.

Inactive protozoa (cysts) may reveal extremely low maintenance rates, but for active cells a specific death rate of 10 year-' has been reported (Clarholm 1985). The present choice of 6 year-' conformed to Hunt et al. (1987). In the laboratory we estimated an assimilation efficiency for protozoa of 0 95 and a production efficiency of 0 40.

Specific death rates of nematodes were considered to be relatively high in Lovinkhoeve soil, due to nematophagous fungi. This mortality factor was established in the laboratory (approximately 13-5 year- ), and subsequently added to the specific



https://www.researchgate.net/publication/222446815_Short-term_bacterial_growth_nutrient_uptake_and_ATP_turnover_in_sterilized_inoculated_and_C-amended_soil_The_influence_of_N_availability?el=1_x_8&enrichId=rgreq-ccaf96c7-e385-40de-8774-34feb3a64392&enrichSource=Y292ZXJQYWdlOzIyMzU2NDM3ODtBUzoxNjE5NjgzNDIxODM5MzdAMTQxNTYyNzY3MTAxMw==

https://www.researchgate.net/publication/250392137_Bacterial_growth_on_macrophyte_leachate_and_fate_of_bacterial_production?el=1_x_8&enrichId=rgreq-ccaf96c7-e385-40de-8774-34feb3a64392&enrichSource=Y292ZXJQYWdlOzIyMzU2NDM3ODtBUzoxNjE5NjgzNDIxODM5MzdAMTQxNTYyNzY3MTAxMw==

100

Nitrogen mineralization in food webs

death rates as used by Hunt et al. (1987). Assimi- lation and production efficiencies were taken from Hunt et al. (1987). C:N ratios of 5 were determined in the laboratory. The preference weighting factors for predatory nematodes feeding on nematodes, protozoa, and bacteria were 100, 10, and 1, respectively (Hunt et al. 1987).

Specific death rates, efficiencies, preference weighting factors (predatory mites preferred microarthropods two to one over nematodes), and C:N ratios of microarthropods were chosen after Hunt et al. (1987).

The specific death rate of enchytraeids was estimated to be approximately 10 year-'. The assimilation efficiency of 0-28 and the production efficiency of 0-4 were taken from Heal & Maclean (1975) with the assumption that enchytraeids are 80% microbivorous and 20% saprophagous.

The specific death rate of 2*4 year-' for earthworms was intermediate between the death rate of 1-5 used for the Kjettslinge (Sweden) food web (Andren et al. 1990) and the death rate of 3-3 used in the Horseshoe Bend (Georgia, USA) food web (Parmelee & Crossley 1988). Assuming that the earthworms were primarily saprophagous, the assimilation efficiency of 0-2 was chosen after Heal & Maclean (1975). The production efficiency of 0-45

was based on calculations of Byzova (1965) and Bostrom (1988).

Results

SIMULATED AND OBSERVED NITROGEN

MINERALIZATION RATES

Annual average nitrogen mineralization

The model calculated the average annual mineralization of nitrogen by each functional group. The total of these rates was compared with the overall soil nitrogen mineralization as estimated in the incubation experiments (Table 2). Microbes accounted for approximately 700/6 of total nitrogen mineralization in both management practices, whereas their contribution to biomass was approximately 95% (Table 1). Among the fauna, amoebae and bacterivorous nematodes were the most important contributors to nitrogen mineralization. Small contributions to nitrogen mineralization were indicated for predatory nematodes, flagellates, enchytraeids and earthworms, while the contributions of the other faunal groups were negligible. Microbes, protozoa and nematodes were indicated to mineralize more nitrogen in the integrated plot

Table 2. Nitrogen mineralization (kg N ha-1 year-1): simulated per functional group, total simulated, and total observed at the integrated (INT) and conventional (CON) management practice and at two depth layers: 0-10 and 10-25 cm. The simulated results were derived from the model, whereas the observed rates were obtained from laboratory incubations of field soil (see Methods section)

Depth layer: 0-10cm 10-25 cm

Management practice: CON INT CON INT

Microbes Bacteria 31-95 52 96 52 39 51-80 Fungi 2 93 4.53 4-86 5 02

Protozoa Amoebae 10-05 13-63 12-01 13-66 Flagellates 0.59 0-82 0.51 0-83

Nematodes Herbivores 0-06 0-36 0 04 0-21 Bacterivores 2 25 6-03 4.33 2-08 Fungivores 0-03 0-05 0 05 0 04 Predators 0-67 1-62 0 65 0-89

Microarthropods Cryptostigmatid mites 0-004 0 0006 0 003 0-0006 Non-cryptostigmatid mites 0-007 0-003 0-007 0-003 Bacterivorous mites 0 004 0.001 0-05 0-0005 Predatory mites 0-02 0.01 0 03 0.01 Nematophagous mites 0-02 0.01 0-06 0 004 Predatory Collembola - 0-01 - 0-02 Fungivorous Collembola 0-08 0-08 0-13 0 10

Annelids Enchytraeids 0 19 0-45 1-12 0 56 Earthworms - 0 47 - 0 26

Total simulated 48-86 81-06 76-23 75-47 Total observed 39 80 59 83






101

P.C. de Ruiter et al.

than in the conventional plot. The opposite was found for microarthropods and enchytraeids.

The simulated overall nitrogen mineralization showed a close resemblance to the observed rates (Table 2). The simulated rates agreed with the observed rates in that nitrogen mineralization was higher in the integrated plot than in the conventional plot. Expressing the overall nitrogen mineralization rates per cm depth, differences between depth layers and management practices became apparent in both observed and simulated mineralization rates (Fig. 3). In the integrated plot proportionally more nitrogen was mineralized in the upper 10cm than in the 10-25cm layer, whereas in the conventional plot no differences between layers were found. Activity of both microbes and fauna, especially nematodes, contributed to the differences in nitrogen mineralization between the two depth layers (Table 2).

Nitrogen dynamics during the growing season

Simulation of the temporal variability in nitrogen mineralization was limited to the contributions of the soil faunawThe model was not designed to simulate the dynamics of nitrogen mineralization by microbes, since the quality (and C:N ratio) of the substrate of microbes may vary considerably during the season, which may lead to alternating periods of net nitrogen mineralization and immobilization (Neeteson, Greenwood & Habets 1986). Simu- lation of the dynamics of nitrogen mineralization by microbes therefore requires a more detailed description of the dynamics of soil organic matter (Van Veen, Ladd & Frissel 1984; Verberne et al.

1990). Results are given for the total fauna and separately for those faunal groups which contributed significantly to nitrogen mineralization (Fig. 4). The observed overall nitrogen mineralization (including mineralization by microbes) to a depth of 25cm ranged from 53 to 82mg N m-2 day-' in the integrated plot, and from 32 to 41mgNm-2 day-' in the conventional plot. The model estimated a faunal contribution to nitrogen mineralization ranging from 8 to 25 mg N m-2 day-' in the integrated plot and from 7 to 18 mg N m-2 day-' in the conventional plot. The contribution of the nematodes showed more temporal and spatial variability than the contribution of the amoebae: in the integrated plot more than 75% of nitrogen mineralization by nematodes occurred in the upper 10 cm.

Effect of group deletion on overall nitrogen mineralization

The results showed that the effect of the removal of a group may exceed considerably the direct contribution of that group to the mineralization of nitrogen (Table 3). For example, the direct contribution of the amoebae was estimated to be approximately 18% of overall nitrogen mineralization (Table 2), whereas deletion of the amoebae resulted in a decrease in nitrogen mineralization of approximately 28%. The contribution of bacterivorous nematodes was estimated to be approximately 5%, whereas deletion led to a decrease in overall nitrogen mineralization of approximately 12%. For predators, differences between direct contribution and the effect of deletion was even more pronounced. For example, the contribution of predatory nema-

N mineralization (kg haw1year1cm depthr1)

10

observed simulated 8

6

4

2

0-10cm 10-25cm 0-10 cm 10-25 cm 0-10 cm 10-25 cm 0-10cm 10-25cm conventional integrated conventional integrated

Fig. 3. Simulated and observed overall nitrogen mineralization under integrated and conventional farming at two different depth layers (0-10cm and 10-25 cm).




Conventional 0-10 cm Integrated 0-10 cm

O bs:13 13 15 17 14 Obs: 41 42 32 27

12 - 12

10 10

6~~~~~~~~~~~~~~

,C 4l

2 2 coJ

E 0 . 0I-- -lo ,, C 18 Aprl 20 June 30 July 19 August 18 November 18April 20 June 30 July 19 August I8 November 0 i Conventional 10-25cm Integrated 10-25cm Cit 14 14 W Obs:19 23 22 24 Obs: 41 38 29 26

12 12 >

Z 10 10 F

6 He,6

4 l4_=

2 2

18 April 20 June 30 July 19 August 18 November 18 Aprl 20 Ju 30 July 9 August 18 Novernber

sampling date (1986) sampling date (1986)

U U* El C Amoebae Flagellates Bacterivrous Predatory Other fauna

nematodes nematodes

Fig. 4. Nitrogen mineralization by the soil fauna during the growing season under integrated and conventional farming at two different depth layers (0-10cm and 10-25 cm). Observed (Obs) total soil nitrogen mineralization (including microbial mineralization) is indicated by quantities at the top of the graphs.

Table 3. Relative reduction (%) in the simulated overall nitrogen mineralization after removal of microbivorous and predatory functional groups from the webs at the integrated (INT) and conventional (CON) management practice and at two depth layers

0-10cm 10-25cm Depth layer: Management practice: CON INT CON INT

Microbivores Amoebae 39-8 32-4 30-8 35 4 Flagellates 2-3 1-9 1-3 2-1 Bacterivorous nematodes 10-3 17-3 12 9 6-4 Fungivorous nematodes 1-3 1-2 1-3 1.1 Cryptostigmatid mites 0-04 0-01 0-02 0-01 Non-cryptostigmatid mites 0.1 0-03 0-06 0-03 Bacterivorous mites 0-02 04003 0-14 0-002 Fungivorous Collembola 1-0 0-8 1.1 0 9 Enchytracids 2-1 3-2 8-2 4-2

Predators Predatory nematodes 12-6 19-1 8-9 8-4 Predatory mites 0-9 0-3 1-0 0-4 Nematophagous mites 0-4 0-2 0-8 0-1 Predatory Collembola - 04 - 0-5

todes was estimated to be approximately 1-4%, whereas deletion reduced overall nitrogen mineralization by approximately 12%. Deletion of each of the other functional groups led to a decrease in

overall nitrogen mineralization that exceeded their simulated direct contribution, but their overall impact remained low.




SENSITIVITY ANALYSIS

In the present sensitivity analysis a change in a parameter value was related to the change in overall nitrogen mineralization, expressed as a percentage or as kgNha-l year-' (0-25cm). Predictably, the outcome of the model was most sensitive to variation in the values of parameters related to those groups of organisms that contributed significantly to nitrogen mineralization: microbes, protozoa, bacterivorous and predatory nematodes. Varying (increasing or decreasing) their biomass by 50% resulted in relative changes in overall nitrogen mineralization of approximately 17, 16, 6, 5, 1-5 and 1% for amoebae, bacteria, predatory nematodes, bacterivorous nematodes, fungi, and flagellates, respectively. Variation in the specific death rates resulted in similar changes in overall nitrogen mineralization (see equation 1). The outcome of the model was more sensitive to production efficiency than to assimilation efficiency. For example, varying the assimilation efficiency by 25% resulted in relative changes in overall nitrogen mineralization of 0% for microbes and 5% for amoebae, whereas varying the production efficiency by 25% resulted in relative changes of approximately 40% for bacteria, 10% for amoebae, 5% for bacterivorous nematodes, 4% for predatory nematodes and 2% for fungi. Nitrogen mineralization per trophic interaction was found to be more sensitive to the C:N ratio of the prey than to the C:N ratio of the consumer; consequently, the output of the model was found to be relatively independent of C:N ratios of the faunal groups but very sensitive to the C:N ratios of microbes and their substrate. In the present sensitivity analysis, the C:N ratios of the substrate and microbes were varied independently of each other, although the C:N ratio of bacteria may vary with fluctuations in the C:N ratio of the substrate (Tezuka 1990). Increasing the C:N ratio of the substrate by 50% resulted in a decrease of approximately 60%, i.e. 75kgNha-1 year-' in the conventional practice and 93kgNha-1 year-' in the integrated practice. Decreasing the C:N ratio of the substrate by 50% resulted in an increase in overall nitrogen mineralization of approximately 175%, i.e. 220kgNha- year-1 in the conventional practice and 280 kg N ha-' year-' in the integrated practice. Increasing the C:N ratio of the bacteria by 50% resulted in an increase in overall nitrogen mineralization of approximately 20%, i.e. 27kgNha-1 year-' in the conventional practice and 33 kg N ha-l year-' in the integrated practice. Decreasing the C:N ratio of the bacteria by 50% resulted in a decrease in overall nitrogen mineralization of approximately 65%, i.e. 80kgNha-' year-' in the conventional practice and 100 kg Nha'l yearly in the integrated practice.

Discussion

Aggregation of species into collections of func- tionally related trophic groups played a central role in the present food web model. The trophic groups were defined mainly by principal food choice, but also by differences in growth rate and mode of feeding (Moore, Walter & Hunt 1988). The validity of this kind of grouping is supported by the simulation results of Gardner, Cale & O'Neill (1982), which suggested that species may be grouped if they have similar diets, predators, growth rates and survival rates. Also, a statistical analysis of the biomass dynamics of the functional groups in the Lovinkhoeve food webs showed that functional groups with similar food choice exhibited similar dynamics, rather than taxonomically related functional groups (Moore & De Ruiter 1991). A possible weakness of the use of functional groups is that the grouping is somewhat arbitrary. For example, the present food webs distinguished amoebae from flagellates, whereas other food web descriptions, such as those from Horseshoe Bend by Hendrix et al. (1987) and from Kjettslinge by Andren et al. (1990), combined amoebae and flagellates into one group of protozoa. The present food web descriptions may also have suffered from incomplete observations of some groups, such as macroarthropods, and trophic interactions, such as those between earthworms and microbes. However, this incompleteness may not necessarily have had a large effect on the calculations, assuming that only species with relatively low densities or weak interactions were missed or neglected.

The results showed that. the present food web model (as proposed by Hunt et al. 1987) was able to calculate soil nitrogen mineralization rates re- sembling the observed rates. The simulated mineralization rates reflected differences between the integrated and conventional management practices. The relatively high content of soil organic material in the integrated practice (Kooistra, Lebbink & Brussaard 1989) was probably responsible for the relatively high rate of nitrogen mineralization in the integrated plot as compared with the conventional plot. The observed and simulated differences between fields and depth layers might have been caused by the differences in depth of tillage (25 cm plough in the conventional plot and 12 cm cultivator in the integrated plot) combined with the high input of organic material in the integrated plot (Kooistra, Lebbink & Brussaard 1989) leading to the higher mineralization rate in the 0-10cm layer. The dif- ference between upper and lower depth layers in the integrated plot was more pronounced with respect to nitrogen mineralization than with respect to biomass: the ratio between upper layer and lower layer for biomass was (per cm depth) approximately 1-~25:1, whereas for nitrogen mineralization it was




approximately 1 6:1. Among the fauna, nematodes contributed considerably to the differences between depth layers, especially during summer. Soil fumigation in the conventional plot (on 18 September 1986) reduced nematode numbers considerably (Brussaard et al. 1990), but did not affect nitrogen mineralization significantly.

The close resemblance between simulated and observed nitrogen mineralization was obtained without parameter optimization (i.e. the adjustment of the parameters to values which give a 'best fit' between model predictions and observations). However, the sensitivity analysis showed that the outcome of the model is very sensitive to parameter values such as the C:N ratio of the substrate, the C:N ratio of the microbes, and the biomass and natural death rates of microbes and protozoa. Esti- mates of these parameters were sometimes uncertain, especially when they were derived from literature data showing wide ranges. The sensitivity for the values of the C:N ratios of the soil organic matter and bacteria may have been overestimated. Bacteria may vary their C:N ratio according to fluctuations in the C:N ratio of the substrate; for example, by the storage of carbon (Tezuka 1990). In that case, changes in the C:N ratio of the soil organic matter, and consequently the C:N ratio of the bacteria, will not affect the allocation of nitrogen by bacteria to production and mineralization as strongly as indicated by the sensitivity analysis.

Estimates of the nitrogen mineralization rate per functional group showed that microbes accounted for approximately 70% of total nitrogen mineralization. This is similar to the percentage given for nitrogen mineralization by microbes in the detrital food web from the Colorado shortgrass prairie (Hunt et al. 1987). Among the faunal groups, amoebae and bacterivorous nematodes were indicated to be the most important contributors to nitrogen mineralization as was also found in the soil food web from the shortgrass prairie. The relatively high mineralization by amoebae was due to the high biomass and high turnover rates of these organisms in both the Lovinkhoeve food webs and the shortgrass prairie food web. Nitrogen mineralization by nematodes was high in the Lovinkhoeve webs because of high turnover rates, whereas in the shortgrass prairie web it was high because of high abundance. Microarthropods and enchytraeids were found in relatively high numbers in the conventional plot, and consequently, their contribution to nitrogen mineralization was higher in the conventional plot than in the integrated plot. This was not ex- pected, because the reduced soil tillage and the high organic matter content in the integrated plot should provide better conditions for these faunal groups than in the conventional plot. Earthworms were not found in the conventional plot, probably due to the

low input of decomposable organic matter in this plot (Marinissen & Van den Bosch 1991).

The impact of the functional groups on the mineralization of nitrogen, expressed as the change in overall nitrogen mineralization after the deletion of the group from the web, exceeded the direct contributions to nitrogen mineralization. The effect of group deletion was connected with the trophic position of the group in the web. This was illustrated by the predatory nematodes which had a much lower biomass than bacterivorous nematodes, yet their impact on overall nitrogen mineralization exceeded the impact of the bacterivores. It must be stressed, however, that the results of this a priori analysis depended strongly on the assumptions underlying the simulation, especially the assumed stimulating effect of predation on the growth rates at the lower trophic levels. For microbivores, this assumption was based on experimental findings indicating that the presence of grazers and predators on microbes may stimulate microbial activity (Coleman et al. 1978; Coleman, Reid & Cole 1983; Woods et al. 1982). It remains uncertain, however, especially for the predators at the higher trophic levels, to what extent these assumptions apply in reality. For comparison we also simulated the case in which predation did not stimulate the growth rates of the prey; in that case the effect of group deletion on nitrogen mineralization was approximately the same as the simulated direct contribution of the group to nitrogen mineralization.

In conclusion, the present simulation model enabled the evaluation of the contributions of functional groups of soil organisms to soil nitrogen mineralization at different soil management practices. However, the food web approach required field and laboratory data for many groups of organisms whereas some of these groups contributed negligibly to nitrogen mineralization. On the other hand, Van Veen & Kuikman (1990) argued that both soil struc- ture and food web interactions might have a con- siderable effect on microbial turnover and nitrogen mineralization. Further developments of the model should therefore include descriptions of food webs in different soils, and a more detailed description of soil organic matter dynamics.

Acknowledgments

We thank E.O. Biewenga, M. ten Cate, M. Geurs, G.H.J. Hoenderboom, and the staff of the Lovink- hoeve experimental farm for technical assistance, and J.A. van Veen, H.G. van Faassen, M. van Noordwijk, and two anonymous referees for com- ments on an earlier version of the manuscript. This work was supported by the Netherlands Integrated Soil Research Programme.




References

Anderson, R.V., Coleman, D.C. & Cole, C.V. (1981) Effect of saprophytic grazing on net mineralization. Terrestrial Nitrogen Cycles (ed. F.E. Clark & T. Ross- wall). Ecological Bulletins, 33, 201-215.

Anderson, T.-H. & Domsch, K.H. (1985) Determination of ecophysiological maintenance requirements of soil organisms in a dormant state. Biology and Fertility of Soils, 1, 81-89.

Andr6n, O., Lindberg, T., Bostrom, U., Clarholm, M., Hansson, A.-C., Johansson, G., Lagerlof, J., Paustian, K., Persson, J., Petterson, R., Schnurer, J., Sohlenius, B. & Wivstad, M. (1990) Organic carbon and nitrogen flows. Ecology of arable land - organisms, carbon, and nitrogen-cycling (ed. 0. Andr6n, T. Lindberg, K. Paustian & T. Rosswall). Ecological Bulletins, 40, 85- 125.

Biaath, E., Lohm, U., Lundgren, B., Rosswall, T., Soder- strom, B. & Sohlenius, B. (1981) Impact of microbial- feeding animals on total soil activity and nitrogen dynamics: a soil microcosm experiment. Oikos, 37, 257-264.

Borkott, H. (1989). Elementgehalte (C, N, P, K) wirbel- loser Bodentiere. Zeitschift fUr Pflanzenerndhrung und Bodenkunde, 152, 77-80.

Bostrtm, U. (1988) Ecology of earthworms in arable land: population dynamics and activity in four cropping systems. Department of Ecology and Environmental Research, Report 34, Swedish University of Agricultural Sciences, Uppsala.

Brussaard, L., van Veen, J.A., Kooistra, M.J. & Lebbink, G. (1988) The Dutch programme on soil ecology of arable farming systems. I. Objectives, approach and preliminary results. Ecological Implications of Contem- porary Agriculture, Proceedings of the Fourth European Ecology Symposium, 8-12 September 1986 at Wageningen (ed. H. Eijsackers & A. Quispel). Ecological Bulletins, 39, 35-40.

Brussaard, L., Bouwman, L.A., Geurs, M., Hassink, J. & Zwart, K.B. (1990) Biomass, composition and temporal dynamics of soil organisms of a silt loam soil under conventional and integrated management. Netherlands Journal of Agricultural Science, 38, 283-302.

Brussaard, L., Kools, J.P., Bouwman, L.A. & De Ruiter, P.C. (1991) Population dynamics and nitrogen mineralization rates in soil as influenced by bacterial grazing nematodes and mites. Proceedings of the Xth Inter- national Soil Zoology Colloquium, Bangalore, India, 7-13 August 1988, pp. 517-523.

Byzova, J.B. (1965) Comparative rate of respiration in some earthworms. Revue d'?cologie et de Biologie du Sol, 2, 207-216.

Clarholm, M. (1985) Possible role for roots, bacteria, protozoa, and fungi in supplying nitrogen to plants. Ecological Interactions in Soil: Plants, Microbes and Animals (ed A.H. Fitter, D. Atkinson, D.J. Read & M.B. Usher), pp. 355-365. British Ecological Society Special Publications, Oxford.

Coleman, D.C., Reid, C.P.P. & Cole, C.V. (1983) Bio- logical strategies of nutrient cycling in soil ecosystems. Advances in Ecological Research, 13, 1-55.

Coleman, D.C., Anderson, R.V., Cole, C.V., Elliott, E.T., Woods, L. & Campion, M.K. (1978) Trophic interactions in soils as they affect energy and nutrient dynamics. IV. Flows of metabolic and biomass carbon. Microbial Ecology, 4, 373-380.

Edwards, C.A. (1990) The importance of integration in sustainable agricultural systems. Sustainable Agricultural

Systems (ed. C.A. Edwards, R. Lal, P. Madden, R.H. Miller & G. House). Soil and Water Conservation Society, Ankeny, Iowa (50021 USA).

Elliott, E.T., Cole, C.V., Fairbanks, B.C., Woods, L.E., Bryant, R.J. & Coleman, D.C. (1983) Short-term bacterial growth, nutrient uptake and ATP turnover in sterilized, inoculated and C-amended soil: the influence of N availability. Soil Biology and Biochemistry, 15, 85-91.

Findlay, S., Carlough, L., Crocker, M.T., Gill, H.K., Meyer, J.L. & Smith, P.J. (1986) Bacterial growth on macrophyte leachate and fate of bacterial production. Limnology and Oceanography, 31, 1335-1341.

Gardner, R.H., Cale, W.G. & O'Neill, R.V. (1982) Robust analysis of aggregation error. Ecology, 63, 771-779.

Hassink, J., Oude Voshaar, J.H., Nijhuis, E.H. & Van Veen, J.A. (1991) Dynamics of the microbial populations of a reclaimed-poldef soil under conventional and a reduced-input farming systems. Soil Biology and Biochemistry, 23, 515-524.

Heal, O.W. & MacLean, S.F. (1975) Comparative productivity in ecosystems-secondary productivity. Unifying Concepts in Ecology (ed. W.H. van Dobben & R.H. Lowe-McConnell), pp. 89-108. Junk, Wageningen.

Hendrix, P.F., Crossley, D.A. Jr, Coleman, D.C., Par- melee, R.W. & Beare, M.H. (1987) Carbon dynamics in soil microbes and fauna in conventional and no-tillage agroecosystems. INTECOL Bulletin, 15, 59-63.

Hunt, H.W., Coleman, D.C., Ingham, E.R., Ingham, R.E., Elliott, E.T., Moore, J.C., Rose, S.L., Reid, C.P.P. & Morley, C.R. (1987) The detrital food web in a shortgrass prairie. Biology and Fertility of Soils, 3, 57-68.

Ingham, E.R., Trofymow, J.A., Ames, R.N., Hunt, H.W., Morley, C.R., Moore, J.C. & Coleman, D.C. (1986) Trophic interactions and nitrogen cycling in a semi-arid grassland soil II. System responses to removal of different groups of soil microbes or fauna. Journal of Applied Ecology, 23, 615-630.

Kooistra, M.J., Lebbink, G. & Brussaard, L. (1989) The Dutch programme on soil- ecology of arable farming systems. 2. Geogenesis, agricultural history, field site characteristics and present farming systems at the Lovinkhoeve experimental farm. Agriculture, Eco- systems and Environment, 27, 361-387.

Kurath, G. & Morita, R.Y. (1983) Starvation-survival physiological studies of a marine Pseudomonas sp. Applied and Environmental Microbiology, 45, 1206-1211.

Marinissen, J.C.Y. & Van den Bosch, F. (1992) Coloniz- ation of new habitats by earthworms: a model approach. Oecologia (in press).

Moore, J.C. & De Ruiter, P.C. (1991) Temporal and spatial heterogeneity of trophic interactions within belowground food webs. Agriculture, Ecosystems and Environment, 34, 371-397.

Moore, J.C., Walter, D.E. & Hunt, H.W. (1988) Arthropod regulation of micro- and mesobiota in belowground food webs. Annual Review of Entomology, 33, 419-439.

Neeteson, J.J., Greenwood, D.J. & Habets, E.J.M.H. (1986) Dependence of soil mineral N on N-fertilizer application. Plant and Soil, 91, 417-420.

O'Connor, F.B. (1955) Extraction of enchytraeid worms from a coniferous forest soil. Nature (London), 175, 815-816.

O'Neill, R.V. (1969) Indirect estimation of energy fluxes in animal food webs. Journal of Theoretical Biology, 22, 284-290.

Parker, L.W., Santos, P.F., Phillips, J. & Whitford,




W.G. (1984) Carbon and nitrogen dynamics during the decomposition of litter and roots of chihuahuan desert annual, Lepidium lasiocarpum. Ecological Monographs, 54, 339-360.

Parmelee, R.W. & Crossley, D.A. Jr (1988) Earthworm production and role in the nitrogen cycle of a no-tillage agroecosystem on the Georgia Piedmont. Pedobiologia, 32, 353-361.

Parton, W.J., Stewart, J.W.B. & Cole, C.V. (1988) Dynamics of C, N, P and S in grassland soils. A model. Biogeochemistry, 5, 109-131.

Tezuka, Y. (1990) Bacterial regeneration of ammonium and phosphate as affected by the carbon:nitrogen:phos- phorus ratio of organic substrates. Microbial Ecology, 19, 227-238.

Van Faassen, H.G. & Lebbink, G. (1990) Nitrogen cycling in high-input versus reduced-input arable farming. Nether- lands Journal of Agricultural Science, 38, 265-282.

Van Veen, J.A. & Kuikman, P.J. (1990) Soil structural aspects of decomposition of organic matter by microbes. Biogeochemistry, 11, 213-233.

Van Veen, J.A. & Paul, E.A. (1979) Conversion of bio- volume measurements of soil organisms, grown under various moisture tensions, to biomass and their nutrient content. Applied and Environmental Microbiology, 37, 686-692.

Van Veen, J.A., Ladd, J.N. & Frissel, M.J. (1984) Model- ling C and N turnover through the microbial biomass in soil. Plant and Soil, 76, 257-274.

Verberne, E.L.J., Hassink, J., De Willigen, P., Groot, J.J.R. & Van Veen, J.A. (1990) Modelling organic matter dynamics in different soils. Netherlands Journal of Agricultural Science, 38, 221-238.

Woods, L.E., Cole, C.V., Elliott, E.T., Anderson, R.V. & Coleman, D.C. (1982) Nitrogen transformations in soil as affected by bacterial-microfaunal interactions. Soil Biology and Biochemistry, 14, 93-98.

Received 13 August 1991; revision received 23 April 1992



simulation of dynamics in nitrogen mineralisation in the belowground food webs of two arable farming...

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